Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data
This addresses privacy risks for users of GPS-enabled devices by showing how mobility data can uniquely identify individuals, even with new data, though it is incremental in applying existing methods to location privacy.
The paper tackles the problem of identifying individuals from GPS mobility data by analyzing the uniqueness of GPS information across three datasets and evaluating the discriminatory power of speed, direction, and distance. It presents a simple technique that effectively identifies users from location data not in the training set, raising privacy concerns.
One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of "significant places", thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.